Performance assessment of Kinect as a sensor for pothole imaging and metrology

Kamal, Khurram; Mathavan, Senthan; Zafar, Tayyab; Moazzam, Imran; Ali, Ahsan; Ahmad, Shafeeq; Rahman, Mujib

doi:10.1080/10298436.2016.1187730

Cited by 30 publications

(13 citation statements)

References 6 publications

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“…Khurram et al [110] used Kinect to predict and analyze the depth and volume of pothole, the mean percentage error are 2.58% and 5.47%, respectively. In addition, the test performance of pothole with water, dust and oil is also discussed.…”

Section: ) Factors Affecting 3d Pavement Defect Detectionmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

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Section: ) Factors Affecting 3d Pavement Defect Detectionmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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“…To include depth information in distress severity measurement, researchers proposed 3D pavement imaging based on Kinect sensor [13], [14], laser scanning [3], [20] and stereovision [22]- [24]. Hou et al [15] explained the development of Digital Highway Data Vehicle (DHDV) at University of Arkansas.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, laser equipment is very expensive, costing in the excess of US$1 million. Likewise, Kamal et al [14] proposed the Kinect sensor technology for pavement depth evaluation and have reported the error percentage of 5.47% for volume calculation of artificial pothole like indentations. In case of stereovision technology, the accuracy of 3 mm is achieved with the error percentage of 6.72% for pothole evaluation [22].…”

Section: E Benchmarking With Artificial Indentations In Concretementioning

confidence: 99%

Pothole 3D Reconstruction With a Novel Imaging System and Structure From Motion Techniques

Ahmed

Ashfaque

Ulhaq

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Machine vision based evaluation systems are receiving increased attention, day by day, for automated quality inspection of roads. Industrial pavement scanners consist of laser scanners and are very expensive, hence inaccessible for everyone. The proposed work presents a simple and novel approach for 3D reconstruction of potholes for an automated inspection and road surface evaluation. The technique utilizes a Structure from Motion based 3D reconstruction algorithm, along with laser triangulation, to generate 3D point clouds of potholes. Alongside, a novel low-cost system, consisting of a single camera and a laser pointer, is also proposed. Keypoint matching techniques are employed, with the 5-point algorithm, on successive image frames to generate a point cloud. However, this point cloud is not metric yet, without scale information. The scale ambiguity is solved by making use of the laser pointer, and using the principle of triangulation. The laser spot is also detected in the same image sequence that is used for point-cloud building, cutting down the image capturing and processing overhead. The system has been benchmarked on artificial indentations with known dimensions, proving the robustness of the measurement scheme and hardware. Static and dynamic tests have been performed. The mean depth errors for measurement made by the imager statically and at dynamic speeds of 10 km/hr, 15 km/hr, and 20 km/hr are 5.3%, 7.9%, 14.4%, and 26.6%, whereas for perimeter the errors are 5.2%, 6.83 %, 11.8%, and 27.8%. The proposed, low-cost technique shows promising results in generating 3D point clouds for potholes.

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“…They evaluated the Kinect performance in measuring the dimensions of potholes in different conditions (water versus oily water). The results showed that the mean percentage error of Kinect for measuring the depth is approximately 3% [41]. Chen et al (2016) used multiple Kinect sensors to evaluate the road surface condition in a dynamic mode.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor

Mahmoudzadeh

Golroo

Jahanshahi

et al. 2019

Sensors

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Measuring pavement roughness and detecting pavement surface defects are two of the most important tasks in pavement management. While existing pavement roughness measurement approaches are expensive, the primary aim of this paper is to use a cost-effective and sufficiently accurate RGB-D sensor to estimate the pavement roughness in the outdoor environment. An algorithm is proposed to process the RGB-D data and autonomously quantify the road roughness. To this end, the RGB-D sensor is calibrated and primary data for estimating the pavement roughness are collected. The collected depth frames and RGB images are registered to create the 3D road surfaces. We found that there is a significant correlation between the estimated International Roughness Index (IRI) using the RGB-D sensor and the manual measured IRI using rod and level. By considering the Power Spectral Density (PSD) analysis and the repeatability of measurement, the results show that the proposed solution can accurately estimate the different pavement roughness.

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Performance assessment of Kinect as a sensor for pothole imaging and metrology

Cited by 30 publications

References 6 publications

Review of Pavement Defect Detection Methods

Review of Pavement Defect Detection Methods

Pothole 3D Reconstruction With a Novel Imaging System and Structure From Motion Techniques

Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor

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